Generalizability of Choice Architecture Interventions
Bibliographic record
Abstract
Although a given choice architecture intervention (‘nudge’) can be highly effective in some conditions, it may be ineffective in others and counter-productive in yet others. Critically, one cannot reliably predict which of these outcomes will happen. In this review, we argue that the average effectiveness of choice architecture interventions in influencing behavior is modest, and there is substantial heterogeneity in their impact. The complex interaction of multiple moderators and their dynamic change over time, makes it difficult to learn about when and to what extent choice architecture interventions work. We outline the obstacles to understanding generalizability, clarify the dimensions of generalizability and review the research practices (systematic exploration and measurement of moderators; sampling, designing, analyzing and reporting for generalizability) that could help the field more efficiently accumulate evidence. We conclude that adopting these practices is essential for advancing nuanced theories and for more accurately predicting the effectiveness of choice architecture interventions across diverse populations, settings, treatments, outputs and analytical approaches.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.446 | 0.651 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.017 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".